Encoder and Decoder Module: Designing efficient decoder and refinement modules is essential for the depth estimation problem [1]}, [2]}. A common practice is to create large and complex decoders to produce accurate depth maps with sharp edges and fine details. However, we argue that by iteratively fusing relevant depth measurements from the 3D points with appearance features from image pixels, we can significantly reduce the size of our decoder and refinement designs. That is, our decoder and refinement modules have only two convolutional layers for each component. To simplify further, we use the same decoder and refinement designs for all Fusion-Nets.
